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<table width="100%" summary="page for beav2"><tr><td>beav2</td><td align="right">R Documentation</td></tr></table>

<h2>
Body Temperature Series of Beaver 2
</h2>

<h3>Description</h3>


<p>Reynolds (1994) describes a small part of a study of the long-term
temperature dynamics of beaver <EM>Castor canadensis</EM> in
north-central Wisconsin.  Body temperature was measured by telemetry
every 10 minutes for four females, but data from a one period of less
than a day for each of two animals is used there.
</p>


<h3>Usage</h3>

<pre>
beav2
</pre>


<h3>Format</h3>


<p>The <code>beav2</code> data frame has 100 rows and 4 columns.
This data frame contains the following columns:
</p>

<dl>
<dt><code>day</code></dt><dd>
<p>Day of observation (in days since the beginning of 1990),
November 3&ndash;4.
</p>
</dd>
<dt><code>time</code></dt><dd>
<p>Time of observation, in the form <code>0330</code> for 3.30am.
</p>
</dd>
<dt><code>temp</code></dt><dd>
<p>Measured body temperature in degrees Celsius.
</p>
</dd>
<dt><code>activ</code></dt><dd>
<p>Indicator of activity outside the retreat.
</p>
</dd>
</dl>



<h3>Source</h3>


<p>P. S. Reynolds (1994) Time-series analyses of beaver body temperatures.
Chapter 11 of
Lange, N., Ryan, L., Billard, L., Brillinger, D., Conquest, L.
and Greenhouse, J. eds (1994)
<EM>Case Studies in Biometry.</EM> New York: John Wiley and Sons.
</p>


<h3>References</h3>


<p>Venables, W. N. and Ripley, B. D. (2002)
<EM>Modern Applied Statistics with S.</EM> Fourth edition.  Springer.
</p>


<h3>See Also</h3>


<p><code>beav1</code>
</p>


<h3>Examples</h3>

<pre>
attach(beav2)
beav2$hours &lt;- 24*(day-307) + trunc(time/100) + (time%%100)/60
plot(beav2$hours, beav2$temp, type = "l", xlab = "time",
   ylab = "temperature", main = "Beaver 2")
usr &lt;- par("usr"); usr[3:4] &lt;- c(-0.2, 8); par(usr = usr)
lines(beav2$hours, beav2$activ, type = "s", lty = 2)

temp &lt;- ts(temp, start = 8+2/3, frequency = 6)
activ &lt;- ts(activ, start = 8+2/3, frequency = 6)
acf(temp[activ == 0]); acf(temp[activ == 1]) # also look at PACFs
ar(temp[activ == 0]); ar(temp[activ == 1])

arima(temp, order = c(1,0,0), xreg = activ)
dreg &lt;- cbind(sin = sin(2*pi*beav2$hours/24), cos = cos(2*pi*beav2$hours/24))
arima(temp, order = c(1,0,0), xreg = cbind(active=activ, dreg))

library(nlme)
beav2.gls &lt;- gls(temp ~ activ, data = beav2, corr = corAR1(0.8),
                 method = "ML")
summary(beav2.gls)
summary(update(beav2.gls, subset = 6:100))
detach("beav2"); rm(temp, activ)
</pre>


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